日本語で技術的な原稿（書籍の章、記事、解説文）を書く・推敲するときは、以下の規範に従う。

既存システムの実装を「事実上の仕様」とみなし、それを形式化することで 「テストでは踏めないバグ」と「実装が暗黙に決めている仕様」を炙り出すための手順書。 仕様書が無い / あてにならない / 仕様と実装がずれている、という現場を前提にする。

Each of these commands will run an ad hoc http static server in your current (or specified) directory, available at http://localhost:8000. Use this power wisely.

Discussion on reddit.

$ python -m SimpleHTTPServer 8000

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

A plain-language writeup by Vuk Rosić of the paper "Probabilistic Tiny Recursive Model" (Sghaier, Parviz & Jolicoeur-Martineau, Mila — arXiv:2605.19943). Independent summary, not affiliated with or endorsed by the authors.

A 7-million-parameter model just beat an ensemble of 7 frontier LLMs on reasoning puzzles — about 10,000× cheaper, and with zero retraining.

Tiny Recursive Models (TRM) solve a puzzle by repeatedly refining a hidden "working state" and their current best answer with the same small two-layer network. But that refinement is deterministic: one input always traces one path, and that path can settle on a wrong answer with no way out. The paper shows many of TRM's failures are runs trapped in a bad "basin" — a region of the hidden space that decodes to a wrong answer and that the deterministic loop can't escape.